AI is promoting inclusivity, reducing inequalities, and enabling data-driven policies for equitable social and economic advancement. In this chart we observe the several aspects of coverage of social insurance programs (% of population).

This perspective offers a real-time view of the worldwide news on inequality-related topics, providing us with a media perspective on the progress at the different fronts of SDG 10.

 

Developed in collaboration with 

A SDG Live Report is what it is all about! Here, you can have a real-time perspective of the state of the world in regard to AI and sustainability, tracking some of the main issues to address in the near future.

AI has significantly influenced the progress of Sustainable Development Goal 10 (SDG 10), which aims to reduce inequality within and among countries. Between 2015 and 2023, there have been 170,218 scientific publications examining the role of AI in addressing various forms of inequality. These studies highlight AI’s potential to identify and mitigate biases in decision-making processes, enhance access to education and healthcare, and support economic inclusion for marginalized communities. AI-driven analytics can uncover patterns of discrimination and provide insights to develop more equitable policies. Media exposure, with 18,009 news articles, reflects a growing public interest in the intersection of AI and social justice, emphasizing the need for ethical AI practices. The development of 808 AI policies targeting SDG 10 indicates a commitment from policymakers to leverage AI for promoting equality, focusing on ensuring that AI technologies are used to benefit all segments of society without exacerbating existing disparities.

Looking ahead, the next 5 to 10 years are poised to see significant advancements in the application of AI to reduce inequalities. AI will play a crucial role in enhancing the inclusivity of education and healthcare systems, providing personalized learning and health solutions that cater to the needs of disadvantaged groups. Efforts to address algorithmic biases will intensify, ensuring that AI systems are fair and transparent. Policymakers will likely introduce more regulations to promote the ethical use of AI, focusing on protecting vulnerable populations from potential harm. Additionally, AI can support economic empowerment by enabling access to financial services and employment opportunities for underrepresented communities. As collaboration between governments, tech companies, and civil society organizations strengthens, AI-driven initiatives will increasingly align with the goals of SDG 10, fostering a more equitable and inclusive global society.

 

Developed in collaboration with the European Commission project 

Here is your window to the global efforts to reduce inequality through AI. This dashboard is configurable and can be integrated into other systems to bring actionable technology into the systems of research institutions, governments and enterprises that are willing to make a difference.

 

 

Developed in collaboration with the European Commission project 

Avoiding data bias in AI systems is crucial to ensure fair, accurate, and equitable outcomes, preventing the reinforcement of existing inequalities and enabling more trustworthy and inclusive technologies. Here you will see a dashboard analysing the bias related to the data ingested in this observatory for SDG 10.

For analysis we use OECD AI Policy documents. Some of those documents are very large, and we split each document into smaller parts (so called “chunks”), which can contain multiple paragraphs. The reason for this is to prepare data for easier analysis with large language models, so called Retrieval-Augmented Generation (RAG). RAG is an advanced technique that combines retrieval-based methods with generative models to improve the performance of tasks such as question answering, text generation, and other natural language processing (NLP) applications. For each chunk then the sentiment is computed based on VADER (Valence Aware Dictionary and sEntiment Reasoner) methodology. Since VADER is known to have weak multilingual capabilities, all the documents were machine translated into English first.

While the results of this procedure are reliant not only upon the accuracy of the sentiment analysis tool, but also upon the accuracy of machine translation, it is important to stress that sentiment analysis is less sensitive to common machine translation problems than other usages, because sentiment analysis usually focuses on identifying the polarity (positive, negative, neutral) of a text rather than understanding its full semantic content. Also, sentiments in text are often expressed redundantly, which can help mitigate the impact of translation errors. As a result, minor translation errors that do not alter the overall sentiment and do not significantly impact the sentiment analysis is possible.

For the purpose of this analysis, they computed the average sentiment of (chunks of) AI policy documents for each country. We are presenting the visualisation of average sentiment of countries’ AI policy documents on the map. Since AI policy documents are mostly documents of legal nature (acts, policies, regulatory and governance frameworks), the sentiment should be mostly neutral, however, the analysis shows that there are country differences.

VADER computes positive, negative and neutral sentiment. Each of those values are between 0 and 1. The score indicates the proportion of text that is considered positive, negative and neutral. The sum of negative, positive, and neutral sentiment scores always equals 1, however in practice the sum of three sentiment scores can sometimes slightly exceed or fall below 1 due to floating-point precision errors or rounding issues that occur during computation.

 

Developed in collaboration with the European Commission project